In gamrot/godley: Stock-Flow-Consistent Model Simulator

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(magrittr)

This guide explains how to use the godley package to create the SIM model, the simplest model with government money, as described by Wynne Godley and Marc Lavoie in Chapter 3 of Monetary Economics: An Integrated Approach to Credit, Money, Income, Production and Wealth.

library(godley)

Base scenario

Start by initializing an empty SFC (Stock-Flow Consistent) model:

# Create empty model
model_sim <- create_model(name = "SFC SIM")

Define the variables for the model:

# Add variables
model_sim <- model_sim |>
  add_variable("C_d", desc = "Consumption demand by households") |>
  add_variable("C_s", desc = "Consumption supply") |>
  add_variable("G_s", desc = "Government supply") |>
  add_variable("H_h", desc = "Cash money held by households") |>
  add_variable("H_s", desc = "Cash money supplied by the government") |>
  add_variable("N_d", desc = "Demand for labor") |>
  add_variable("N_s", desc = "Supply of labor") |>
  add_variable("T_d", desc = "Taxes, demand") |>
  add_variable("T_s", desc = "Taxes, supply") |>
  add_variable("Y", desc = "Income = GDP") |>
  add_variable("Yd", desc = "Disposable income of households") |>
  add_variable("alpha1", init = 0.6, desc = "Propensity to consume out of income") |>
  add_variable("alpha2", init = 0.4, desc = "Propensity to consume out of wealth") |>
  add_variable("theta", init = 0.2, desc = "Tax rate") |>
  add_variable("G_d", init = 20, desc = "Government demand") |>
  add_variable("W", init = 1, desc = "Wage rate")

Establish the relationships between variables by adding equations:

# Add equations
model_sim <- model_sim |>
  add_equation("C_s = C_d", desc = "Consumption") |>
  add_equation("G_s = G_d") |>
  add_equation("T_s = T_d") |>
  add_equation("N_s = N_d") |>
  add_equation("Yd = W * N_s - T_s") |>
  add_equation("T_d = theta * W * N_s") |>
  add_equation("C_d = alpha1 * Yd + alpha2 * H_h[-1]") |>
  add_equation("H_s = G_d - T_d + H_s[-1]") |>
  add_equation("H_h = Yd - C_d + H_h[-1]") |>
  add_equation("Y = C_s + G_s") |>
  add_equation("N_d = Y/W") |>
  add_equation("H_s = H_h", desc = "Money equilibrium", hidden = TRUE)

Now, you can simulate the model (in this example, we calculate the baseline scenario over 100 periods using the Newton method)

# Simulate model
model_sim <- simulate_scenario(model_sim,
  scenario = "baseline", max_iter = 350, periods = 100,
  hidden_tol = 0.1, tol = 1e-05, method = "Newton"
)

With the simulation estimated, visualize the results for the variables of interest:

# Plot results
plot_simulation(
  model = model_sim, scenario = c("baseline"), from = 1, to = 50,
  expressions = c("Y", "C_d", "G_s")
)

Note: The above example uses the new pipe operator (|>), which requires R 4.1 or later.

Shock scenario

With godley package you can simulate how shocks affect the economy (specifically, how they impact the base scenario).

In this example, we apply a permanent increase in government expenditures.

First, initialize an empty shock object:

# Create empty shock
shock_sim <- create_shock()

Define the shock by adding an appropriate equation:

# Add shock equation with increased government expenditures
shock_sim <- add_shock(shock_sim,
  variable = "G_d",
  value = 25,
  desc = "Increase in government expenditures", start = 5, end = 50
)

Integrate the shock into the model by creating a new scenario:

# Create new scenario with this shock
model_sim <- add_scenario(model_sim,
  name = "expansion", origin = "baseline", shock = shock_sim
)

Simulate the scenario with the shock applied:

# Simulate shock
model_sim <- simulate_scenario(model_sim,
  scenario = "expansion", max_iter = 350, periods = 100,
  hidden_tol = 0.1, tol = 1e-05, method = "Newton"
)

Finally, compare the results of the base scenario with those of the shock scenario.

# Plot results
plot_simulation(
  model = model_sim, scenario = c("baseline", "expansion"), from = 1, to = 50,
  expressions = c("Y")
)

plot_simulation(
  model = model_sim, scenario = c("baseline", "expansion"), from = 1, to = 50,
  expressions = c("C_d")
)

plot_simulation(
  model = model_sim, scenario = c("baseline", "expansion"), from = 1, to = 50,
  expressions = c("G_s")
)

References

For more details on SIM model, refer to Chapter 3 of Monetary Economics: An Integrated Approach to Credit, Money, Income, Production and Wealth.

gamrot/godley documentation built on April 12, 2025, 1:50 p.m.